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2005
ACM

Coresets in dynamic geometric data streams

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Coresets in dynamic geometric data streams
A dynamic geometric data stream consists of a sequence of m insert/delete operations of points from the discrete space {1, . . . , ∆}d [26]. We develop streaming (1 + )-approximation algorithms for k-median, k-means, MaxCut, maximum weighted matching (MaxWM), maximum travelling salesperson (MaxTSP), maximum spanning tree (MaxST), and average distance over dynamic geometric data streams. Our algorithms maintain a small weighted set of points (a coreset) that approximates with probability 2/3 the current point set with respect to the considered problem during the m insert/delete operations of the data stream. They use poly( −1 , log m, log ∆) space and update time per insert/delete operation for constant k and dimension d. Having a coreset one only needs a fast approximation algorithm for the weighted problem to compute a solution quickly. In fact, even an exponential algorithm is sometimes feasible as its running time may still be polynomial in n. For example one can compute in p...
Gereon Frahling, Christian Sohler
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where STOC
Authors Gereon Frahling, Christian Sohler
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